ANN-QSAR model for virtual screening of androstenedione c-skeleton containing phytomolecules and analogues for cytotoxic activity against human breast cancer cell line MCF-7

Combinatorial Chemistry and High Throughput Screening

Volumn 16, Issue 1, 2013, Pages 57-72

  Prakash, Om ^a   Khan, Feroz ^a   Sangwan, Rajender Singh ^a   Misra, Laxminarain ^a  

^a CSIR CENTRAL INSTITUTE OF MEDICINAL AND AROMATIC PLANTS   (India)

Author keywords

Androstenedione; ANN; MCF 7; QSAR; Withanolide

Indexed keywords

4BETA HYDROXY WITHANOLIDE E; ANDROSTENEDIONE DERIVATIVE; ANTINEOPLASTIC AGENT; AROMATASE; CID 16757497; CID 301751; CID 390666; CID 46830222; CID 73621; REDUCED NICOTINAMIDE ADENINE DINUCLEOTIDE FERRIHEMOPROTEIN REDUCTASE; UNCLASSIFIED DRUG; WITHANOLIDE;

ANTINEOPLASTIC ACTIVITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; BINDING AFFINITY; BINDING SITE; BIOASSAY; CANCER CELL CULTURE; COMPUTER MODEL; CRYSTALLIZATION; CYTOTOXICITY; DEVELOPMENTAL TOXICITY; DRUG ABSORPTION; DRUG ACTIVITY; DRUG BINDING; DRUG BIOAVAILABILITY; DRUG CYTOTOXICITY; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG SCREENING; HUMAN; HUMAN CELL; KIDNEY CLEARANCE; MOLECULAR DOCKING; MUTAGENICITY; PHARMACOPHORE; PLASMA PROTEIN BINDING; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; TOXICITY TESTING;

EID: 84872919886     PISSN: 13862073     EISSN: 18755402     Source Type: Journal    
DOI: 10.2174/1386207311316010008     Document Type: Article

References (35)

1. Edwards, P. Peptoid positional scanning libraries for identification of multidrug resistance reversal agents. Drug Discov. Today, 2006, 11, 669-670.
2. Burchenal, J. H.; Holmberg, E. A. The utility of resistant leukaemias in screening for chemotherapeutic activity. Ann. N Y Acad Sci., 1958, 76, 826-829.
3. Helguera, A. M.; Rodriguez-Borges, J. E.; Garcia-Mera, X.; Fernandez, F.; Natalia, M.; Cordeiro, D. S. Probing the anticancer activity of nucleoside analogues: A QSAR model approach using an internally consistent training set. J. Med. Chem., 2007, 50, 1537-1545.
4. Wang, Y.H.; Li, Y.; Li, Y.H.; Yang, S. L.; Yang, L. Modeling K-m values using electrotopological state: Substrates for cytochrome P450 3A4-mediated metabolism. Bioorg. Med. Chem. Lett., 2005, 15, 4076-4084.
5. Habibi-Yangjeh, A.; Danandeh-Jenagharad, M.; Nooshyar, M. Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR. J. Mol. Model., 2006, 12, 338-347.
6. Siu, F. M.; Che, C. M. Quantitative structure-activity (affinity) relationship (QSAR) study on protonation and cationization of alpha-amino acids. J. Phys. Chem. A, 2006, 110 (12), 348-354.
7. Su, Q.; Zhou, L. QSAR modeling of AT1 receptor antagonists using ANN. J. Mol. Model., 2006, 12, 869-875.
8. Aoyama, T.; Suzuki, Y.; Ichikawa, H. Neural networks applied to pharmaceutical problems. III. Neural networks applied to quantitative structure-activity relationship (QSAR) analysis. J. Med. Chem., 1990, 33, 2583-2590.
9. González-Arjona, D.; López-Pérez, G.; Gustavo González, A. Non-linear QSAR modeling by using multilayer perceptron feedforward neural networks trained by back-propagation. Talanta, 2002, 56, 79-90.
10. Mize, C. D.; Abbott, A. M.; Gacasan, S.B.; Parrill, A.L.; Baker, D. L. Ligand-based autotaxin pharmacophore models reflect structurebased docking results. J. Mol. Graph. Mod., 2011, 31, 76-86.
11. Pal, R.; Islam, M. A.; Hossain, T.; Saha, A. Molecular Modeling on Structure-Function Analysis of Human Progesterone Receptor Modulators. Scientia Pharmaceutica, 2011, 79 (3), 461-477.
12. Taha, M. O.; Tarairah, M.; Zalloum, H.; Abu-Sheikha, G. Pharmacophore and QSAR modeling of estrogen receptor beta ligands and subsequent validation and in silico search for new hits. J. Mol. Graph. Mod., 2010, 28, 383-400.
13. Shao, L.; Wu, L.; Fan, X.; Cheng, Y. Consensus ranking approach to understanding the underlying mechanism with QSAR. J. Mol. Graph. Mod., 2010, 50(11), 1941-1948.
14. Reddy, A. S.; Pati, S. P.; Kumar, P. P.; Pradeep, H. N.; Sastry, G. N. Virtual Screening in Drug Discovery-A Computational Perspective. Curr. Protein Pept. Sci., 2007, 8(3), 331-353.
15. Stander, X. X.; Stander, B. A.; Joubert, A. M. In vitro effects of an in-silico modelled 17?-estradiol derivative in combination with dichloroacetic acid on MCF-7 and MCF-12A cells. Cell Prolif., 2011, 44(6), 567-581.
16. Keseru, G.M.; Molnar, L.; Greiner, I. A Neural Network Based Virtual High Throughput Screening Test for the Prediction of CNS Activity. Comb. Chem. High Throughput Screen., 2000, 3(6), 535-540.
17. Yap, C. W. PaDEL-Descriptor: Open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 2011, 32(7), 1466-1474.
18. Votano, J. R.; Parham, M., Hall, L. M.; Hall, L. H.; Kier, L. B.; Oloff, S. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J. Med. Chem., 2006, 49, 7169-7181.
19. Hagan, M. T.; Menhaj, M. B. Training feed forward networks with the Marquardt algorithm. Neural Networks, IEEE Transactions, 1994, 5, 989-993.
20. Prakash, O.; Mehrotra, S.; Krishna, A.; Mishra, B. N. A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures. J. Theor. Biol., 2010, 265, 579-585.
21. Trott, O.; Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem., 2010, 31, 455-461.
22. Benfenati, E.; Benigni, R.; Demarini, D.M.; Helma, C.; Kirkland, D.; Martin, T.M.; Mazzatorta, G.; Ouedraogo-Arras, G.; Richard, A.M.; Schilter, B.; Schoonen, W.G.E.J.; Snyder, R.D.; Yang, C. Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives. J. Environ. Sci. Health, 2009, Part C, 27(2), 57-90.
23. Cassano, A.; Manganaro, A; Martin, T.; Young, D.; Piclin, N.; Pintore, M.; Bigoni, D.; Benfenati, E. CAESAR models for developmental toxicity. Chem. Cent. J., 2010, 4(Suppl 1), S4.
24. Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A. K.; Cherkasov, A.; Li, J.; Gramatica, P.; Hansen, K.; Schroeter, T.; Müller, K.-R.; Xi, L.; Liu, H; Yao, X.; Öberg, T.; Hormozdiari, F.; Dao, F.; Sahinalp, C.; Todeschini, R.; Polishchuk, P.; Artemenko, A.; Kuz'min, V.; Martin, T.M.; Young, D. M.; Fourches, D.; Muratov, E.; Tropsha, A.; Baskin, I.; Horvath, D.; Marcou, G.; Varnek, A; Prokopenko, V. V.; Tetko, I.V. Applicability domains for classification problems: benchmarking of distance to models for AMES mutagenicity set. J. Chem. Inf. Model, 2010, 50, 2094-2111.
25. Zhu, H.; Martin, T.M.; Young, D. M.; Tropsha, A. Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure. Chem. Res. Toxicol., 2009, 22 (12), 1913-1921.
26. Hong, Y.; Rashid, R.; Chen, S.; Binding features of steroidal and nonsteroidal inhibitors. Steroids, 2011, 76(8), 802-806.
27. Wu, J.; Mei, J.; Wen, S.; Liao, S.; Chen, J.; Shen Y. A self-adaptive genetic algorithm-artificial neural network algorithm with leaveone-out cross validation for descriptor selection in QSAR study. J. Comput. Chem., 2010, 31(10), 1956-1968.
28. Hodgson, J. ADMET-turning chemicals into drugs. Nat. Biotechnol., 2001, 19, 722-726.
29. Norinder, U.; Bergström, C. A. S. Prediction of ADMET properties. Chem. Med. Chem., 2006, 1, 920-937.
30. Sean, E.; Sergey, A.; Andy, R.; Eugene, K.; Eugene, A.; Rakhmatulin, S. S.; Bugrim, A.; Nikolskaya, T. A combined approach to drug metabolism and toxicity assessment. Drug Metab. Dispos., 2006, 34, 495-503.
31. Reichel, A.; Begley, D. J. Potential of immobilized artificial membranes for predicting drug penetration across the blood-brain barrier. Pharm. Res., 1998, 15, 1270-1274.
32. Lombardo, F.; Gifford, E.; Shalaeva, M. Y. In silico ADME prediction: Data, models, facts and myths. Mini-Rev. Med. Chem., 2003, 3, 861-875.
33. Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46, 3-26.
34. Veber, D.F.; Stephen, R.; Johnson; Cheng, H.Y.; Smith, B. R.; Keith, W.; Ward; Kopple, K. D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45, 2615-2623.
35. Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. J. Pharm. Sci., 1999, 88, 807-814.